Methods and systems for providing digital therapeutics for treating mental health disorders using machine learning

ABSTRACT

A system and method for developing a treatment plan using multi-stage machine learning. A method includes identifying at least one cognitive distortion of a user by applying a first machine learning model to a first portion of features extracted from data related to the user, wherein the first machine learning model is a cognitive distortions model trained using training user-created content; determining a plurality of digital therapeutics exercise tasks for the user based on the at least one cognitive distortion by applying a second machine learning model to a second portion of the features extracted from the data related to the user and to the output of the first machine learning model, wherein the second machine learning model is a task recommender model trained using training cognitive distortions and the training user-created content; and generating a treatment plan including the plurality of digital therapeutics exercise tasks for the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/014,990 filed on Apr. 24, 2020, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to rehabilitation of mentalhealth issues using digital therapeutics, and more specifically tomachine learning techniques enabling such rehabilitation using digitaltherapeutics.

BACKGROUND

Many people face challenges with mental health disorders. Mental healthdisorders may have particularly negative effects on professionals thatwork in fields where stress is high and cognitive abilities are crucialto high quality, efficient work production. For example, legalprofessionals face high demand for production of work requiring sharpmemory and critical thinking. These challenges may affect performance,which in turn may further contribute to exacerbating or initiatingmental health issues such as anxiety and depression.

Digital therapeutics are software-based treatments that have directimpacts on illnesses or diseases. Digital therapeutics provide solutionsfor preventing, managing, or treating health conditions, either alone orin combination with non-digital treatments.

It would therefore be advantageous to provide new solutions for applyingdigital therapeutics for the rehabilitation of mental health disorders.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” or “certain embodiments” may be used herein to refer to asingle embodiment or multiple embodiments of the disclosure.

Certain embodiments disclosed herein include a method for developing atreatment plan using multi-stage machine learning. The method comprises:identifying at least one cognitive distortion of a user, whereinidentifying the at least one cognitive distortion wherein identifyingthe at least one cognitive disorder of the user further comprisesapplying a first machine learning model to a first portion of featuresextracted from data related to the user, wherein the first machinelearning model is a cognitive distortions model trained using traininguser-created content; determining a plurality of digital therapeuticsexercise tasks for the user based on the at least one cognitivedistortion, wherein determining the plurality of digital therapeuticsexercise tasks further comprises applying a second machine learningmodel to a second portion of the features extracted from the datarelated to the user and to the output of the first machine learningmodel, wherein the second machine learning model is a task recommendermodel trained using training cognitive distortions and the traininguser-created content; and generating a treatment plan including theplurality of digital therapeutics exercise tasks for the user.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon causing a processingcircuitry to execute a process, the process comprising: identifying atleast one cognitive distortion of a user, wherein identifying the atleast one cognitive distortion wherein identifying the at least onecognitive disorder of the user further comprises applying a firstmachine learning model to a first portion of features extracted fromdata related to the user, wherein the first machine learning model is acognitive distortions model trained using training user-created content;determining a plurality of digital therapeutics exercise tasks for theuser based on the at least one cognitive distortion, wherein determiningthe plurality of digital therapeutics exercise tasks further comprisesapplying a second machine learning model to a second portion of thefeatures extracted from the data related to the user and to the outputof the first machine learning model, wherein the second machine learningmodel is a task recommender model trained using training cognitivedistortions and the training user-created content; and generating atreatment plan including the plurality of digital therapeutics exercisetasks for the user.

Certain embodiments disclosed herein also include a system fordeveloping a treatment plan using multi-stage machine learning. Thesystem comprises: a processing circuitry; and a memory, the memorycontaining instructions that, when executed by the processing circuitry,configure the system to: identify at least one cognitive distortion of auser, wherein identifying the at least one cognitive distortion whereinidentifying the at least one cognitive disorder of the user furthercomprises applying a first machine learning model to a first portion offeatures extracted from data related to the user, wherein the firstmachine learning model is a cognitive distortions model trained usingtraining user-created content; determine a plurality of digitaltherapeutics exercise tasks for the user based on the at least onecognitive distortion, wherein determining the plurality of digitaltherapeutics exercise tasks further comprises applying a second machinelearning model to a second portion of the features extracted from thedata related to the user and to the output of the first machine learningmodel, wherein the second machine learning model is a task recommendermodel trained using training cognitive distortions and the traininguser-created content; and generate a treatment plan including theplurality of digital therapeutics exercise tasks for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein, and other objects, features, andadvantages of the disclosed embodiments will be apparent from thefollowing detailed description taken in conjunction with theaccompanying drawings.

FIG. 1 is a network diagram utilized to describe various disclosedembodiments.

FIG. 2 is a flowchart illustrating a method for treating mental healthdisorders using machine learning according to an embodiment.

FIG. 3 is a flow diagram illustrating a machine learning hierarchyaccording to an embodiment.

FIG. 4 is a flow diagram illustrating a machine learning schemaaccording to an embodiment.

FIG. 5 is a flowchart illustrating a method of treating target mentalhealth disorders using digital therapeutics according to an embodiment.

FIG. 6 is a schematic diagram of a treatment system according to anembodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

It has been identified that assigning specific mental health exercisesto patients having particular types of cognitive distortions canalleviate the effects of those cognitive distortions and symptoms ofunderlying mental health disorder. Additionally, the information neededto diagnose patients with mental health disorders can be more readilyobtained by guiding patients through use of social media or otherplatforms where users create content that may reflect their mentalstate. To this end, the disclosed embodiments provide techniques fortreating mental health disorders using specific sets of digitaltherapeutics exercise tasks.

Additionally, it has been identified that the types of content providedvia user-created content platforms may be more effectively and rapidlyprocessed via automated solutions as compared to manual analysis ofcontent by medical health professionals. In particular, the sheer volumeof content may hinder treatment by medical health professionals whereautomated solutions would allow for effectively providing treatment. Tothis end, the disclosed embodiments provide techniques for identifyingmental health disorders of users and determining treatments for thosemental health disorders using machine learning that allows forautomating diagnosis and treatment. Further, in order to improveaccuracy of the machine learning models applied for theseidentifications and determinations, the disclosed embodiments includemulti-stage, synergistic machine learning techniques that provide moreaccurate diagnoses and corresponding treatments as compared to othermachine learning schemas.

The various disclosed embodiments include a method and system fortreating mental health disorders using machine learning. Features areinput to a digital therapeutics treatment machine learning model, whichoutputs data indicating digital therapeutics to be prescribed to a userfor treatment. The prescribed digital therapeutics include a set ofdigital therapeutics exercise tasks. In an embodiment, the prescribeddigital therapeutics output by the digital therapeutics machine learningmodel are administered to the user, for example, via a user interface ofa user device.

The input features include at least an identifier of one or morecognitive distortions of the user, and may further include data relatedto prior digital therapeutics prescribed to the user. The cognitivedistortion identifiers, the data related to prior digital therapeutics,or both, are determined by applying one or more machine learning modelsto data related to interactions of the user with a digital therapeuticsplatform. The interactions may include, but are not limited to, posts ofthe user, responses of the user, both, and the like.

In an embodiment, machine learning is performed via a hierarchy ofmachine learning models in order to generate a treatment plan for usersexperiencing one or more mental health disorders manifesting ascognitive distortions. The machine learning hierarchy provides multiplestages of machine learning, where the outputs of one stage are used asan input for another stage. In a further embodiment, a first stageincludes application of a cognitive distortions model and a taskperformance model. The outputs of those models are used, among otherfeatures, as features to be input to a digital therapeuticsrecommendation model during the second stage. The hierarchy providesmore accurate models at each subsequent stage, thereby resulting in amore accurate model for generating treatment plans than a model trainedbased on the various inputs in one stage.

In another embodiment, additional machine learning models may be trainedfor aspects of treatment in addition to the digital therapeuticsexercise task digital therapeutics. Such additional aspects of treatmentmay include, but are not limited to, prescribing group therapy, formaland informal peer support, prescribing virtual reality sessions,prescribing pharmaceuticals, or a combination thereof in tandem with thedigital therapeutics.

The treatment plan created based on the outputs of the machine learningmodels described above effectively serves as an intervention curriculumincluding a set of interventions designed to improve mental healthwithin a peer support setting. The set of interventions may furtherinclude a multi-faceted set of interventions providing various means ofengagement. Each intervention is an assigned exercise designed to allowfor addressing mental health individually and within a group. Thetreatment plan is created based on a set of exercises targeted toward aspecific goal. Relevant actions for a particular user are determinedusing the machine learning models noted above based on the engagementand needs of those users.

The various disclosed embodiments also include a method for treatment ofmental health disorders using digital therapeutics. A set of digitaltherapeutics is determined for a patient based on a type of mentalhealth disorder the patient has been diagnosed with. The set of digitaltherapeutics includes digital therapeutics selected from a category ofdigital therapeutics corresponding to the type of mental healthdisorder. In an embodiment, the digital therapeutics include assignmentof digital therapeutics exercise tasks via a user interface.

In some embodiments, the treatment may further include determining adiagnosis of a mental health disorder and applying digital therapeuticsbased on the diagnosis. To this end, the treatment may include applyingone or more of the machine learning models to data related to useractivity in order to determine the diagnosis. The digital therapeuticsto be applied are determined based on the diagnosis and then applied,for example, via a user interface of a user device used by the patient.The diagnoses and treatment plan may be updated, for example,periodically or when new data is provided. The updated diagnoses may beutilized to provide targeted exercises based on portions of thediagnosis (e.g., the treatment plan may be updated to includedepression-related exercises when depression scores above a thresholdare determined).

In some embodiments, the set of digital therapeutics are realized ascontent provided via a specific diagnostic-based channel with a specificgoal. The channel may be selected (e.g., by a user), and may be providedas recommendations. The channel may be limited to users who have beenassigned or recommended that channel such that all users using thechannel have similar symptomology in the form of the same or relatedmental health disorder, occupation, position, combinations thereof, andthe like. By providing the treatment plan in a channel with other usershaving the same or related mental health disorders, each user isprovided with a relevant peer support experience. Further, the user'scommunication with other users in the peer group may be utilized asadditional data which can be analyzed (for example, using machinelearning techniques as described herein) in order to further improveassessment and, consequently, to improve accuracy of treatment plangeneration.

FIG. 1 shows an example network diagram 100 utilized to describe thevarious disclosed embodiments. In the example network diagram 100, auser device 120, a digital therapeutics treatment system 130, and aplurality of databases 140-1 through 140-N (hereinafter referred toindividually as a database 140 and collectively as databases 140, merelyfor simplicity purposes) are communicatively connected via a network110.

The network 110 may be, but is not limited to, a wireless, cellular orwired network, a local area network (LAN), a wide area network (WAN), ametro area network (MAN), the Internet, the worldwide web (WWW), similarnetworks, and any combination thereof.

The user device (UD) 120 may be, but is not limited to, a personalcomputer, a laptop, a tablet computer, a smartphone, a wearablecomputing device, or any other device capable of receiving anddisplaying data related to digital therapeutics. In an exampleimplementation, the user device 120 includes one or more input/output(I/O) devices 125. The I/O devices 125 are configured to provide a userinterface for providing digital therapeutics to the user (e.g., viadisplay of prescribed digital therapeutics exercises and confirmation ofcompletion by the user).

The user device 120 may also be utilized for interactions of a user witha digital therapeutics platform implemented by one or more servers 150.To this end, the user device 120 may have installed thereon a softwareapplication (app) 127. The digital therapeutics platform may be a socialmedia platform connecting users including the user of the user device120. User content data may include user content such as posts,responses, messages (e.g., chat messages), and other content uploaded toor otherwise stored in the social media platform.

The digital therapeutics treatment system 130 is configured to at leastdetermine treatments using machine learning as described herein. In someembodiments, the digital therapeutics treatment system 130 is furtherconfigured to send content for display on the user device 120 as part ofadministration of the digital therapeutics treatment.

It should be noted that FIG. 1 merely shows an example environment inwhich the disclosed techniques may be implemented, but that thedisclosed embodiments are not restricted to the network diagram shown inFIG. 1. In particular, in some implementations, at least some of thetechniques described herein may be performed by the user device 120. Forexample, instructions for performing at least some of the disclosedembodiments (e.g., embodiments including applying machine learningmodels) may be implemented via the application 127). This may be useful,for example, in order to ensure privacy by processing data anddetermining diagnoses locally on the user device 120. In addition, theadministration of treatment techniques described herein may, in someimplementations, be performed by a doctor.

FIG. 2 is a flowchart illustrating a method for treating mental healthdisorders using machine learning according to an embodiment. In anembodiment, the method is performed by the digital therapeutics system130, FIG. 1. In another embodiment, the method is performed by the userdevice 120, FIG. 1.

At optional S210, data related to a user is collected. In an embodiment,the collected data includes at least data related to user-createdcontent platforms (e.g., social media platforms) such as, but notlimited to, a mental health disorder treatment platform, open sourceInternet forums, other open source language models, or a combinationthereof. In a further embodiment, the collected data may further includedata related to other users (e.g., other users of the same platformsfrom which the user-related data is collected).

At S220, features are extracted from the data related to a user. Thedata related to a user at least includes content created by the user. Inan embodiment, such content includes textual content such as posts,responses, messages, and the like. In an example implementation, suchcontent is content provided via a social media platform.

In an embodiment, the extraction of features may further includederiving features by applying artificial neural networks as part of deeplearning. In particular, language-based features derived from contentcreated by the user (e.g., posts, comments, replies, social mediaparticipation such as “ask me anything” threads, and chat messages) maybe derived at least partially using deep learning. This derivation oflanguage-based features using deep learning allows for more accurate andmore precise extraction of features from natural language, which in turnimproves the results of the subsequent machine learning stages.

At S230, a first stage of machine learning is applied to at least afirst portion of the extracted features. In an embodiment, S230 at leastincludes identification of cognitive distortions of the user via acognitive distortion model. Such identification may further indicate ascore or other representation of a degree of severity for the mentalhealth disorder. In a further embodiment, S230 may further includedetermining task performance for tasks previously performed by the uservia a task performance model. Each of the cognitive distortion model andthe task performance model is trained based on respective trainingfeatures.

In an embodiment, the first stage of machine learning may be at leastpartially implemented using classifiers. In particular, the cognitivedistortions model may classify data into one or more potential mentalhealth disorders of the user.

At S240, a second stage of machine learning is applied to at least asecond portion of the extracted features and the outputs of the firststage of machine learning. In an embodiment, S240 at least includesdetermination of parameters for a treatment plan for the user via a taskrecommender model. The treatment plan includes digital therapeuticsexercise tasks to be prescribed to the user, and may further includetypes of tasks, specific tasks, acuity level of the user for differenttasks, or a combination thereof.

In some embodiments, transfer learning may be utilized in order toimprove the second machine learning model based on results of training atask recommender model for a group of other users. To this end, a taskrecommender model may be pre-trained based on data related to the otherusers of the group of other users, and the pre-trained task recommendermodel may be used as the starting model for training of what will becomethe task recommender model for the user.

In a further embodiment S240 may also include determining parameters fora conversational agent interacting with the user during treatment usinga conversational parameters model and reconfiguring the conversationalagent based on the determined parameters. Each of the task recommendermodel and the conversational parameters model is trained based onrespective training features.

In an embodiment, the second stage of machine learning may be at leastpartially implemented using classifiers. In particular, the taskrecommender model may classify tasks to be prescribed as treatment intotypes of tasks that are suitable for the user.

At S250, based on the output of the second stage of machine learning, atreatment plan is determined. In an embodiment, the treatment plan is adigital therapeutics treatment plan including assigning, to the user,digital therapeutics exercise tasks via a user device (e.g., the userdevice 120, FIG. 1).

In an embodiment, S250 includes identifying the task types (e.g., basedon a task identifier output during the second stage of machine learning)in a task database, adding the context of the user's challenges to thetask (e.g., based on prior task performance), determining the tasksbased on the identified task types, adding the tasks to a user's taskqueue, and optionally adding the task to similar users' queue. Similarusers may be, but are not limited to, users having similar mental healthdisorders (e.g., users having scores for the same mental healthdisorders above a threshold).

At optional S260, the treatment plan is administered to the user viadigital therapeutics. The treatment plan may be administered via a userdevice which may be configured to, but is not limited to, displayinginstructions for performing the digital therapeutics exercise tasks,receiving confirmation of performance of tasks, receiving additionalcontent based on tasks, combinations thereof, and the like. Suchadditional content may include, but is not limited to, posts, comments,likes, dislikes, multimedia content, or other user-created contentprovided as part of or in response to the treatment plan or a portionthereof.

At optional S270, based on the additional content received duringadministration of the treatment plan, the machine learning models may betrained further. To this end, transfer learning techniques may beutilized to improve the models based on the user's feedback as indicatedin the additional content. More specifically, the natural language andother expressions of behaviors indicated in the additional content areused as feedback to one or more of the machine learning models (e.g.,the cognitive distortion model, the task recommender model, or both),and the resulting models may be used for another user or group of users.In particular, features may be derived as discussed above based on dataindicating a user's progress with digital therapeutics and contentgenerated in response to digital therapeutics. This, in turn, may alterthe types and scores of cognitive distortions determined for the user.

In this regard, it is noted that transfer learning is a research problemfocusing on using knowledge gained while solving one problem andapplying that knowledge to a different but related problem. Differentusers have unique mental health disorders but that related mental healthdisorders that manifest similarly can be utilized to learn moreeffective forms of treatment for other users. Accordingly, transferlearning can be utilized to improve the accuracy of machine learningmodels trained to, for example, determining digital therapeuticsexercise tasks.

In a further embodiment, the transfer learning techniques may beutilized to train the models in order to provide better treatment plansfor similar groups of users. The task recommender model may be trainedbased on features extracted from the additional content generated bymultiple users in the same peer group on any platform in order to trainthe task recommender model to provide recommendations that will improvethe mental well-being of the group via collective participation. To thisend, when training the task recommender for another user or group ofusers, the model trained for the first group of users is utilized as thestarting model for training such that the results of training the modelfor the first group of users can be used to improve the training of thetask recommender model for a different user or group of users. The otheruser or group of users may be a user or group of users having one ormore common attributes such as, but not limited to, occupation, title,similar cognitive distortions, combinations thereof, and the like.

It should be noted that different features are used for differentmachine learning models and during different stages of machine learning.The disclosed embodiments are not limited to the particular flow shownin FIG. 2 with respect to extraction of features. Features may beextracted in a different order from each other, at least some featuresmay be extracted during or after application of one of the machinelearning models. In particular, features not needed for one stage ofmachine learning or for one machine learning model may be extractedduring or after that stage or model without departing from the scope ofthe disclosure.

FIG. 3 is a flow diagram illustrating a machine learning hierarchyaccording to an embodiment.

As depicted in FIG. 3, during a first stage of machine learning,language-based features 311 are input to a cognitive distortions model321 in order to output cognitive distortion features 334. Likewise, tasksuccess features 312 are input to a task performance (“perform.”) model322 in order to output task performance features 335.

During a second stage of machine learning, the cognitive distortionfeatures 334 and the task performance features 335 are input to the taskrecommendation model 340 along with other features in order to outputdata related to a plan 350. The plan 350 includes data to be used informulating a treatment plan including, but not limited to, types oftasks to be assigned to the user, task identifiers of tasks to beassigned to the user, acuity level of the user, or a combinationthereof. In the embodiment shown in FIG. 3, the other features includeuser-specific features 331, activity-based features 332, andcategory-specific features 333. Further descriptions and examples ofeach type of feature used in FIG. 3 is described further below withrespect to FIG. 4.

In some embodiments, the task recommender model 340 or another machinelearning model (not shown in FIG. 3) may be further configured to outputparameters to be used by a conversational agent that will interact withthe user during treatment. To this end, inputs for such machine learningof conversational parameters may include, but are not limited to, thelanguage-based features 311, meta language-based features (not shown inFIG. 3), features indicating challenges identified by the user inconversation (e.g., features extracted by analyzing messages of the userfor challenge-based terminology), and historical user response features.The output conversational parameters may include, but are not limitedto, empathy tone, response type, and response content (e.g., text orother specific content to include).

FIG. 4 is a flow diagram illustrating a machine learning schemaaccording to an embodiment.

In the embodiment shown in FIG. 4, data including user language pooldata 410 and meta language-based features 420 output by deep learning(DL) models are used to derive features (DL model outputs 420), taskdata 430 including prior task results and identifiers. The derivedfeatures include task category-specific language descriptor features440-1, language analysis features 440-2 (e.g., linguistic inquiry typesand word counts), and task success features 440-3. The user languagepool data 410 may include, but is not limited to, posts, comments,replies, and chat data. Meta language-based features include outputs ofa deep learning model such as, but not limited to, sentiment analysis,named entity recognition, topic identification, and tokenization.

The language analysis features 440-2 shown in FIG. 4 may includelinguistic inquiry types, word counts, and deep learning features (e.g.,weights of convolutional and recurrent neural networks). The languageanalysis features 440-2 are input to a cognitive distortions model450-1, which is a machine learning model trained based on historicallanguage analysis features. The cognitive distortions model 450-1outputs types of cognitive distortions and corresponding strength valuesindicating the degree to which the user exhibits each respective type ofcognitive distortion.

The task success features 440-3 are defined separately for differenttasks. The task success features 440-3 are input to a task performancemodel trained based on historical task success features. The taskperformance model 450-2 outputs performance scores indicating a degreeof success on previous tasks.

The cognitive distortions outputs 460-1 and task performance outputs460-2 are input as features to a machine learning model of a taskrecommender system 470. The task recommender system 470 outputs datarelated to digital therapeutics exercise tasks to be prescribed to auser as part of treating the user's cognitive distortions in the form oftask and acuity outputs 480. In the implementation shown in FIG. 4, suchoutputs 480 include the type of each task suitable for the user, tasktypes and identifiers of tasks enjoyed by the user, and acuity level foreach task. Other input features for the task recommender system includeuser-specific variation features, activity-based features, and the taskcategory-specific language descriptors.

In some embodiments (not shown in FIG. 4), the task recommender system470 may further output parameters (not shown) to be used for aconversational agent interacting with the user based at least partiallyon the features discussed with respect to FIG. 4. Examples for suchoutput are described above in the discussion of FIG. 3.

The used-based features 440-4 may include user-specific variationfeatures such as, but not limited to, user preferences (i.e.,preferences of the current user), user-specific features of other users,and task type and identification data. The activity-based features 440-5may include, but are not limited to, a number of tasks completed, anamount of time spent completing each task, a number of clinicalassessments completed, clinical assessment scores for the completedclinical assessments, and task types and identifiers of prior tasks.

FIG. 5 is a flowchart illustrating a method of treating mental healthdisorders according to an embodiment.

At optional S510, one or more mental health disorders of a patient arediagnosed. The diagnosis may be based on clinical evaluation of thepatient, analysis of content created by the patient (e.g., contentuploaded to a social media platform), or a combination thereof. In analternative embodiment, the diagnosis may be predetermined.

In an embodiment, S510 may include applying one or more machine learningmodels, for example as described above. Cognitive distortions of thepatient are identified. The combination of cognitive distortions of thepatient symptomize the mental health disorders of the patient. In afurther embodiment, the mental health disorders may have correspondingscores, and the patient is diagnosed with only mental health disordersbased on cognitive distortions having scores above a threshold (i.e.,mental health disorders which have been determined to be likelyapplicable to the patient).

At S520, a treatment plan is determined based on the mental healthdisorders of the patient. In an embodiment, the treatment plan includesone or more digital therapeutics exercise tasks. Each digitaltherapeutics exercise task belongs to a category of digital therapeuticstasks corresponding to a type of mental health disorder among thediagnosed mental health disorders. In a further embodiment, thetreatment plan is determined based on clinical assessment scores of theuser from previous tasks.

In an example implementation, tasks are divided into three broadcategories: wellbeing assessments, therapy exercises, and communityengagement. In an embodiment, the task exercise categories forcorresponding types of target mental health disorders are assigned asillustrated in Table 1:

TABLE 1 Mental health disorder Category of Task Depression Strengthsidentification Anxiety Mood identification Substance use disorderCompassion Mood and personality disorders Savoring Depression, anxiety,and substance use Cognitive disorder restructuring Depression, anxiety,and substance use Behavioral disorder activation Substance user disorderGratitude

Alternatively or collectively, the criteria for assigned tasks may bespecific mental health disorders or combinations of types of mentalhealth disorders and severities (e.g., as represented by a scoreindicating the degree of the mental health disorder). Further, thespecific tasks to be assigned may be selected from among tasks belongingto the corresponding category based on factors such as, but not limitedto, specific mental health disorders, severity of mental healthdisorder, prior tasks completed by the patients, tasks completed byother patients, combinations thereof, and the like.

In another embodiment, the treatment plan includes a set of digitaltherapeutics exercise tasks to be administered to the patient. The setmay include multiple instances of any or all of the digital therapeuticsexercise tasks, and the digital therapeutics exercise tasks may furtherbe arranged in a particular order. As a non-limiting example the set mayinvolve performing a first digital therapeutics exercise task, a seconddigital therapeutics exercise task, the first digital therapeuticsexercise task again, and a third digital therapeutics exercise task. Ina further embodiment, the treatment plan may be implemented via digitaltherapeutics, for example via a software application installed on a userdevice.

In yet another embodiment, the treatment plan may further include one ormore supplemental prescriptions. The supplemental prescriptions mayinclude, but are not limited to, treatment via a doctor (e.g., apsychiatrist), additional social interaction (e.g., via a social mediaplatform), and the like. In an example implementation, the additionalsocial interaction includes sharing the prescribed digital therapeuticsexercise tasks with a community of the patient's peers, participating inthe peer community by providing self-generated content (e.g., questionsand comments), participating in group therapy or coaching sessions, acombination thereof and the like.

At S530, treatment is administered. The administration at least includesprescribing the treatment plan to the patient. In some implementations,the treatment plan may be administered via a user device (e.g., the userdevice 120, FIG. 1).

A non-limiting example treatment plan follows. This example treatmentplan may be a default treatment plan that is modified based ondeterminations described above based on mental health disordersexhibited by a patient. The example treatment plan includes prescriptionof a series of digital therapeutics exercise tasks over a period ofweeks. The tasks are given descriptions indicating the activities to beundertaken by the user to complete the tasks as well as explanations ofevidence supporting assignment of those particular tasks and suggestedcommunity channels to be used for providing user-created content inresponse to the task assignments.

Week 0 (Initial Assessment) Task 1:

Estimated completion time: 2 minutesTask title: Finish your wellbeing assessmentTask description: You can only improve what you can measure.Task evidence: Over 50 peer-reviewed studies have shown that people whomeasured their wellbeing using a clinical-grade scale like PHQ8 weremore aware of their wellbeing needs and experienced faster improvementsin their wellbeing compared to people who did not.

Week 1 Task 1:

Estimated completion time: 3 minutesTask title: Reinforce your strengthsTask description: Recollect an activity or a thought process that hassupported your wellbeing and share in detail with your community.Task evidence: Identifying what has helped your wellbeing makesrepeating the behavior easier. This is an evidence-based practice forbuilding resilience. This technique is a part of solution-focusedtherapy. Over 70 peer-reviewed studies have demonstrated itseffectiveness in addressing a spectrum of wellbeing challenges includingdepression and substance use disorder.Suggested channels: Aware gym, Depression, Substance Use Disorder

Task 2:

Estimated completion time: 3 minutesTask title: Outline your strengthsTask description: Recollect an activity or a thought process that youhave tried but has NOT worked for your wellbeing and share in detailwith your community.Task evidence: Identifying what has not worked for you helps you outlineyour current strengths better and also makes avoiding those behaviorseasier. Research has shown that clearly outlining your strengths canhelp you access them easily when you need them.Suggested channels: Aware gym, Depression, Substance Use Disorder

Week 2 Task 1:

Estimated completion time: 2 minutesTask title: Rate your moodTask description: Take a moment to breathe. Identify how you arefeeling. Rate your mood from 0-10 (10 being the highest experience ofthat mood). Share this in detail with your community.Task evidence: Identifying and rating how you feel helps improveemotional intelligence. This can help improve relations with peers andclients. This technique is an integral part of Cognitive BehaviorTherapy, which is an evidence-based wellbeing practice supported by over500 research articles.Suggested channels: Aware gym, Depression, Anxiety

Task 2:

Estimated completion time: 3 minutesTask title: Experience compassionTask description: Find a post on the community on your topic of interestand comment on it.Task evidence: Supporting others has therapeutic benefits. Research hasshown that compassion can serve as a buffer against stress by protectingus from stress. In addition, the “pleasure centers” of the brain whichhelp us experience pleasure (e.g. food, sex, money) are also equallyactivated by acts of compassion. Finally, increased compassion isassociated with decreased risk of depression.Suggested channels: Aware gym, Depression, Substance Use Disorder

Week 3 Task 1:

Estimated completion time: 2 minutesTask title: Spot exceptionsTask description: Recollect an overwhelming obstacle that you have facedin the past. When faced with this obstacle, what's your “go to” way toovercome it? Share this in detail with your community.Task evidence: Spotting positive exceptions from the past can helpovercome challenges in the future. Exception finding is designed tobuild long-term resilience, and is an integral part of solution-focusedtherapy. Over 70 peer-reviewed studies have demonstrated theeffectiveness of this therapy in addressing a spectrum of wellbeingchallenges including depression and substance use disorder.Suggested channels: Aware gym, Depression, Anxiety

Task 2:

Estimated completion time: 3 minutesTask title: Savor an experience.Task description: Recollect a positive experience you had in the pastweek and how it made you feel. Can you rate your feeling from 0-10 (10being the highest experience of that mood). Share this in detail withyour community.Task evidence: Savoring positive experiences helps to upregulatepositive emotions. Savoring is the opposite of coping and is a part ofPositive Psychotherapy. Research has shown that savoring can boostcreativity, help build stronger relationships, and decrease depressivesymptoms.

Week 4: Task 1:

Estimated completion time: 3 minutesTask title: Practice self-compassionTask description: What did you say to yourself when the last time youwere self-critical? Think about what you would say to a loved one in thesame situation. Now use these words to rephrase what you say toyourself. Share this in detail with your community.Task evidence: Self-compassion has several therapeutic and occupationalbenefits. Research has shown that self-compassion is associated withimproved conscientiousness, leadership, and resilience as well asdecreased symptoms of stress, depression, and anxiety. Self-compassionpractices are often integrated into Positive Psychotherapy andAcceptance and Commitment Therapy.

Task 2:

Estimated completion time: 3 minutesTask title: DecatastrophizeTask description: Recollect a situation you are fearful or anxiousabout. Describe the worst case, best case, and most likely case scenariooutcome. Reflect on your findings. Share this in detail with yourcommunity.Task evidence: Decatastrophizing helps to effectively overcome worries.It is a technique rooted in Cognitive Behavioral Therapy, which is anevidence-based technique found to be most effective in overcoming fearsand anxiety.

Week 5 Task 1:

Estimated completion time: 4 minutesTask title: Identify behavioral activationsTask description: Take a moment to notice your mood this week. Compareit to your mood last week. Can you remember anything you did that eitherpositively or negatively affected your mood? Please share in detail withyour community.Task evidence: Understanding how your mood is related to your behaviorand vice-versa can help better manage mood using the appropriatebehaviors. Research has shown that this technique, known as behavioralactivation, can be very effective in overcoming several wellbeingchallenges, including depression.

Task 2:

Estimated completion time: 4 minutesTask title: Try gratitudeTask description: Take a moment to remember one blessing that you have.How has this helped you? Please share in detail with your community.Task evidence: Gratitude can be very therapeutic. Research has shownthat it improves confidence, patience, and provides a buffer from stressand depression. Gratitude is generally integrated into PositivePsychotherapy.

FIG. 6 is an example schematic diagram of a digital therapeuticstreatment system 130 according to an embodiment. The treatment system130 includes a processing circuitry 610 coupled to a memory 620, astorage 630, and a network interface 640. In an embodiment, thecomponents of the digital therapeutics treatment system 130 may becommunicatively connected via a bus 650.

The processing circuitry 610 may be realized as one or more hardwarelogic components and circuits. For example, and without limitation,illustrative types of hardware logic components that can be used includefield programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), Application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), graphics processing units (GPUs),tensor processing units (TPUs), general-purpose microprocessors,microcontrollers, digital signal processors (DSPs), and the like, or anyother hardware logic components that can perform calculations or othermanipulations of information.

The memory 620 may be volatile (e.g., random access memory, etc.),non-volatile (e.g., read only memory, flash memory, etc.), or acombination thereof.

In one configuration, software for implementing one or more embodimentsdisclosed herein may be stored in the storage 630. In anotherconfiguration, the memory 620 is configured to store such software.Software shall be construed broadly to mean any type of instructions,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. Instructions may includecode (e.g., in source code format, binary code format, executable codeformat, or any other suitable format of code). The instructions, whenexecuted by the processing circuitry 610, cause the processing circuitry610 to perform the various processes described herein.

The storage 630 may be magnetic storage, optical storage, and the like,and may be realized, for example, as flash memory or other memorytechnology, compact disk-read only memory (CD-ROM), Digital VersatileDisks (DVDs), or any other medium which can be used to store the desiredinformation.

The network interface 640 allows the digital therapeutics treatmentsystem 130 to communicate with the databases 140 for the purpose of, forexample, retrieving data user preference data, retrieving data relatedto prior tasks of users, and the like. Further, the network interface640 allows the digital therapeutics treatment system 130 to communicatewith the user device 120 for the purpose of sending data to be displayedon the user device 120 in order to prescribe and have the user completedigital therapeutics digital therapeutics exercise tasks.

It should be understood that the embodiments described herein are notlimited to the specific architecture illustrated in FIG. 6, and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments.

It should also be noted that, at least in some implementations, at leastsome techniques described herein may be performed via the user device.As a particular example, machine learning models may be stored in theuser device 120 after training and applied by the user device 120. Tothis end, the user device 120 may include any or all of the componentsof FIG. 6 in order to allow for this functionality. Applying the machinelearning models locally on the user device may aid in complying withmedical privacy regulations.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

It should be understood that any reference to an element herein using adesignation such as “first,” “second,” and so forth does not generallylimit the quantity or order of those elements. Rather, thesedesignations are generally used herein as a convenient method ofdistinguishing between two or more elements or instances of an element.Thus, a reference to first and second elements does not mean that onlytwo elements may be employed there or that the first element mustprecede the second element in some manner. Also, unless statedotherwise, a set of elements comprises one or more elements.

As used herein, the phrase “at least one of” followed by a listing ofitems means that any of the listed items can be utilized individually,or any combination of two or more of the listed items can be utilized.For example, if a system is described as including “at least one of A,B, and C,” the system can include A alone; B alone; C alone; 2A; 2B; 2C;3A; A and B in combination; B and C in combination; A and C incombination; A, B, and C in combination; 2A and C in combination; A, 3B,and 2C in combination; and the like.

What is claimed is:
 1. A method for developing a treatment plan usingmulti-stage machine learning, comprising: identifying at least onecognitive distortion of a user, wherein identifying the at least onecognitive distortion wherein identifying the at least one cognitivedisorder of the user further comprises applying a first machine learningmodel to a first portion of features extracted from data related to theuser, wherein the first machine learning model is a cognitivedistortions model trained using training user-created content;determining a plurality of digital therapeutics exercise tasks for theuser based on the at least one cognitive distortion, wherein determiningthe plurality of digital therapeutics exercise tasks further comprisesapplying a second machine learning model to a second portion of thefeatures extracted from the data related to the user and to the outputof the first machine learning model, wherein the second machine learningmodel is a task recommender model trained using training cognitivedistortions and the training user-created content; and generating atreatment plan including the plurality of digital therapeutics exercisetasks for the user.
 2. The method of claim 1, wherein the data relatedto the user includes content created by the user.
 3. The method of claim1, further comprising: administering the treatment plan to the user,wherein administering the treatment plan to the user further comprises:assigning the plurality of digital therapeutics exercise tasks to theuser; and providing data for administering the treatment plan to a userdevice of the user.
 4. The method of claim 3, wherein the user is afirst user, further comprising: training, via transfer learning, atleast the task recommender model based further on feedback received fromthe user device in response to the plurality of digital therapeuticsexercise tasks in order to create a further trained task recommendermodel; and applying the further trained task recommender model tofeatures extracted from data related to at least one second user,wherein the output of the further trained task recommender model is atleast one digital therapeutics task for a group of users including theat least one second user.
 5. The method of claim 1, further comprising:identifying a plurality of task types based on output of the secondmachine learning model, wherein the plurality of digital therapeuticsexercise tasks is determined based on the identified plurality of tasktypes; and creating a task queue for the user, wherein the task queueincludes the plurality of digital therapeutics exercise tasks.
 6. Themethod of claim 1, further comprising: applying a third machine learningmodel to a plurality of features extracted from data indicating a degreeof success of the user with respect to a plurality of training digitaltherapeutics exercise tasks, wherein the output of the third machinelearning model is a plurality of task performance features, wherein thesecond machine learning model is further applied to the plurality oftask performance features.
 7. The method of claim 1, further comprising:determining a plurality of parameters for a conversational agentinteracting with the user during a treatment based on the treatmentplan; and reconfiguring the conversational agent based on the determinedplurality of parameters.
 8. The method of claim 1, further comprising:deriving language-based features from user-created content included inthe data related to the user by applying at least one artificial neuralnetwork to the user-created content, wherein the first portion offeatures includes the language-based features.
 9. A non-transitorycomputer readable medium having stored thereon instructions for causinga processing circuitry to execute a process, the process comprising:identifying at least one cognitive distortion of a user, whereinidentifying the at least one cognitive distortion wherein identifyingthe at least one cognitive disorder of the user further comprisesapplying a first machine learning model to a first portion of featuresextracted from data related to the user, wherein the first machinelearning model is a cognitive distortions model trained using traininguser-created content; determining a plurality of digital therapeuticsexercise tasks for the user based on the at least one cognitivedistortion, wherein determining the plurality of digital therapeuticsexercise tasks further comprises applying a second machine learningmodel to a second portion of the features extracted from the datarelated to the user and to the output of the first machine learningmodel, wherein the second machine learning model is a task recommendermodel trained using training cognitive distortions and the traininguser-created content; and generating a treatment plan including theplurality of digital therapeutics exercise tasks for the user.
 10. Asystem for developing a treatment plan using multi-stage machinelearning, comprising: a processing circuitry; and a memory, the memorycontaining instructions that, when executed by the processing circuitry,configure the system to: identify at least one cognitive distortion of auser, wherein identifying the at least one cognitive distortion whereinidentifying the at least one cognitive disorder of the user furthercomprises applying a first machine learning model to a first portion offeatures extracted from data related to the user, wherein the firstmachine learning model is a cognitive distortions model trained usingtraining user-created content; determine a plurality of digitaltherapeutics exercise tasks for the user based on the at least onecognitive distortion, wherein determining the plurality of digitaltherapeutics exercise tasks further comprises applying a second machinelearning model to a second portion of the features extracted from thedata related to the user and to the output of the first machine learningmodel, wherein the second machine learning model is a task recommendermodel trained using training cognitive distortions and the traininguser-created content; and generate a treatment plan including theplurality of digital therapeutics exercise tasks for the user.
 11. Thesystem of claim 10, wherein the data related to the user includescontent created by the user.
 12. The system of claim 10, wherein thesystem is further configured to: administer the treatment plan to theuser by assigning the plurality of digital therapeutics exercise tasksto the user and providing data for administering the treatment plan to auser device of the user.
 13. The system of claim 12, wherein the user isa first user, wherein the system is further configured to: train, viatransfer learning, at least the task recommender model based further onfeedback received from the user device in response to the plurality ofdigital therapeutics exercise tasks in order to create a further trainedtask recommender model; and apply the further trained task recommendermodel to features extracted from data related to at least one seconduser, wherein the output of the further trained task recommender modelis at least one digital therapeutics task for a group of users includingthe at least one second user.
 14. The system of claim 10, wherein thesystem is further configured to: identify a plurality of task typesbased on output of the second machine learning model, wherein theplurality of digital therapeutics exercise tasks is determined based onthe identified plurality of task types; and create a task queue for theuser, wherein the task queue includes the plurality of digitaltherapeutics exercise tasks.
 15. The system of claim 10, wherein thesystem is further configured to: apply a third machine learning model toa plurality of features extracted from data indicating a degree ofsuccess of the user with respect to a plurality of training digitaltherapeutics exercise tasks, wherein the output of the third machinelearning model is a plurality of task performance features, wherein thesecond machine learning model is further applied to the plurality oftask performance features.
 16. The system of claim 10, wherein thesystem is further configured to: determine a plurality of parameters fora conversational agent interacting with the user during a treatmentbased on the treatment plan; and reconfigure the conversational agentbased on the determined plurality of parameters.
 17. The system of claim10, wherein the system is further configured to: derive language-basedfeatures from user-created content included in the data related to theuser by applying at least one artificial neural network to theuser-created content, wherein the first portion of features includes thelanguage-based features.